Physicians and researchers often need to identify patches of electrically active cortical or myocardial tissue in order to identify a source of illness or to map brain activity. While known monitoring equipment are capable of determining that electrical or magnetic activity has occurred, the determination of a source of that activity must often be calculated or estimated. The process of calculating or estimating the source of electromagnetic activity in tissue is generally referred to as source reconstruction.
There are a number of different methods known in the art for performing source reconstruction. Many of these methods involve creating a model which attempts to determine the source of activity through the use of mathematical formulas which describe electromagnetic field distributions. These formulas typically depend on the position and orientation of the source, the position and orientation of the sensors which pick up the electromagnetic signals, and the geometry and conductivity properties of the volume conductor (head or chest) tissue.
One known method of source reconstruction involves the determination of equivalent current dipoles. This method makes the basic assumption that the source of electromagnetic activity is focal and small in number. However, measured data exhibits a limited Signal-to-Noise Ratio (SNR) due to background activity, environmental and amplifier noise. The noise distribution of the data leads to scattered dipole positions in the source space around the most probable source position. As such, the reconstructed dipoles only represent the most probable source positions.
There is a need for an apparatus and a method to determine and to display an area surrounding the reconstructed dipoles which represent a most probable solution to the source reconstruction model given the noise level in the data. This area is generally known as a confidence interval and it represents a probability distribution which corresponds to the noise level.